The present invention relates to surface coefficients of adhesion and, in particular, to a method and system for estimating the individual wheel surface coefficient of adhesion using tire forces.
Many vehicles are equipped with a variety of electronicallycontrolled chassis control systems, such as traction control systems, anti-lock braking systems, active brake control systems, active rear wheel steering systems, steer-by-wire systems, suspension control systems, etc. The purpose of these control systems is to improve vehicle performance, stabilityxe2x80x94in terms of acceleration, braking and handlingxe2x80x94during demanding driving maneuvers performed close to or at the limit of the adhesion between the individual tires of the vehicle and the road surface. During such maneuvers, tire forces and resulting vehicle dynamic behavior are strongly influenced by the coefficient of adhesion between the individual tires of the vehicle and the road surface. In order to achieve satisfactory performance while driving under almost all road conditions, the control algorithms for the chassis control systems should be able to adapt to changing road surface conditions. Similarly, estimations of tire forces in both the longitudinal and lateral directions could help improve the overall performance of these subsystems. Present algorithms drawn to the estimation of the surface coefficient of adhesion are limited to an average surface coefficient for the entire vehicle. It would be desirable to have an algorithm for estimating the coefficient of adhesion between the road surface and individual tires disposed on a vehicle.
This invention is a method and apparatus for estimating the coefficients of adhesion between the road surface and each tire of a vehicle, as well as tire forces in the longitudinal and lateral directions. The method relies on information about the vehicle and wheel dynamics derived from, for example, steering wheel angle sensor(s), lateral acceleration sensor, yaw rate sensor, wheel speed sensors, and/or estimates of brake torque and driving torque applied to the vehicle wheels, which can be obtained from the brake and powertrain controllers, respectively. The method derives tire longitudinal and lateral forces estimated in two ways: one which depends on the sensor information and vehicle parameters but does not use the surface coefficient of adhesion, and the other depending on the estimated surface coefficients of adhesion. In one aspect of the invention, the estimated surface coefficients of adhesion are reiteratively adjusted, so that the differences between the tire forces calculated by each method are minimized. In another aspect of the invention, the adjustments to the estimated surface coefficients of friction are made according to a speed of adaptation that is determined at least partly in accordance with a determined vehicle handling state.
In a preferred embodiment, tire longitudinal forces are estimated from the wheel dynamics by using measured wheel speeds, brake torque, driving torque, and information about wheel inertia. The tire lateral forces per axle are preferably estimated from vehicle lateral dynamics, using measured yaw rate and lateral acceleration, as well as known vehicle parameters. To perform these calculations, no knowledge of surface conditions is necessary. At the same time, tire longitudinal and lateral forces are preferably calculated using an analytical tire model. The tire model yields longitudinal and lateral tire forces for each tire, which explicitly depend on the estimated surface coefficients of adhesion, but also on the estimated tire normal forces, wheel longitudinal slips, and wheel slip angles. The tire normal forces are preferably estimated using known vehicle parameters, measured lateral acceleration, and estimated longitudinal acceleration, by taking into account the effects of load transfers due to braking/accelerating and cornering. The vehicle and tire slip angles may be estimated simultaneously with the tire forces, using a closed loop dynamic observer. The observer is a model of vehicle dynamics in lateral direction with additional feedback of relevant measured signals. By monitoring the difference between the tire forces calculated from the tire model and estimated from vehicle and wheel dynamics, the algorithm recognizes situations when any of the vehicle tires operates close to the limit of adhesion and adjusts the estimated surface coefficient of adhesion for this tire so as to bring the calculated tire forces closer to the estimated forces.
The method for estimating the surface coefficients of adhesion for individual wheels is a version of a parameter identification algorithm, in which the estimates are adjusted at each iteration, so that they track (adapt to) the changes in the actual surface coefficients. Good tracking performance requires quick adjustments of the estimates, which may result in sensitivity to measurement noise. In order to provide a good balance between the speed of response (tracking performance) and immunity to noise, the speed of adaptation of surface estimation for each wheel is adjusted depending on particular operational conditions of this wheel and the vehicle. Preferably, the speed of adaptation is close to zero when the tire forces (in both longitudinal and lateral direction) remain within the linear range of tire behavior: that is, when the tire forces estimated (from wheel and vehicle dynamics) and calculated from linear and nonlinear tire models are in substantial agreement. The speed of adaptation preferably increases as the errors between the forces calculated from the tire models and the forces estimated (from vehicle and wheel dynamics) increase. In addition, the speed of adaptation preferably varies depending on whether the vehicle is in a steady state or in a quick transient.
As compared to prior art algorithms for estimation of surface coefficients, the proposed algorithm has the following advantages. It facilitates calculation of estimates of surface coefficients for each individual tire, rather than an average value for all four tires. The algorithm permits detection of surface coefficient in a wider range of operating conditions, including braking, accelerating, cornering and any combination thereof. It also permits detection of special situations, such as split mu conditions, in which different tires have significantly different coefficients. In addition, the algorithm provides estimates of all tire forces and tire and vehicle slip angles. All these estimated variables can be used to improve control of active brake, active steering systems, or electronically controlled suspensions, thus improving overall vehicle performance.